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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Related Experiment Video

Updated: Dec 15, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

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Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the

Subina Mehta¹, Caleb W Easterly¹, Ray Sajulga¹

¹Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.

|July 12, 2020

Summary

This study integrates two open-source label-free quantification (LFQ) tools, moFF and FlashLFQ, into the Galaxy platform. This provides researchers with validated tools for accurate peptide and protein quantification in complex multi-omics studies.

Keywords:

galaxy framework label-free quantification proteomics workflows

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Last Updated: Dec 15, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

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Published on: January 10, 2025

Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Area of Science:

Proteomics and Bioinformatics
Computational Biology and Data Science

Background:

Label-free quantification (LFQ) using MS1 intensities is crucial for accurate peptide and protein quantification in proteomics.
LFQ supports diverse applications including metaproteomics and proteogenomics.
Existing Galaxy platform workflows lacked robust and well-tested LFQ tools.

Purpose of the Study:

To evaluate and implement two open-source LFQ tools, moFF and FlashLFQ, within the Galaxy platform.
To optimize and validate the performance of these LFQ tools for complex multi-omics analyses.
To establish a standardized process for software implementation, optimization, and validation in bioinformatics workflows.

Main Methods:

Rigorous evaluation of moFF and FlashLFQ functionalities, including match-between-runs (MBR) and multi-format input.
Implementation of LFQ tools within the Galaxy platform using containers and/or conda packages.
Optimization and validation of software performance for analyzing large-scale datasets.

Main Results:

Successful integration and optimization of moFF and FlashLFQ into the Galaxy platform.
Demonstrated improved usability and accessibility of robust LFQ tools for multi-omics research.
Validated performance characteristics including MBR and handling of various input file formats.

Conclusions:

The integration provides accessible and validated LFQ tools for the Galaxy ecosystem.
This work enhances the capability of the Galaxy platform for advanced proteomics and multi-omics data analysis.
Researchers can now leverage these optimized tools for reliable peptide and protein quantification.